def test_som_reduces_distances():
# SOM functions correctly if is moves neurons towards inputs
input_matrix, target_matrix = datasets.get_xor()
# Small initial weight range chosen so network isn't "accidentally"
# very close to inputs initially (which could cause test to fail)
som_ = som.SOM(2, 4, initial_weights_range=0.25)
# Convenience function
def min_distances():
all_closest = []
for inp_vec in input_matrix:
distances = som_.activate(inp_vec)
all_closest.append(min(distances))
return all_closest
# Train SOM
# Assert that distances have decreased
all_closest = min_distances()
som_.train(input_matrix, target_matrix, iterations=20)
new_closest = min_distances()
print all_closest
print new_closest
for old_c, new_c in zip(all_closest, new_closest):
assert new_c < old_c
def test_SOM_activate_vector():
som_ = som.SOM(2, 2)
som_._weights = numpy.ones(som_._weights.shape)
assert helpers.approx_equal(som_.activate([1, 1]), [0, 0])
assert helpers.approx_equal(som_.activate([0, 1]), [1, 1])
assert helpers.approx_equal(som_.activate([1, 0]), [1, 1])
assert helpers.approx_equal(som_.activate([1, 2]), [1, 1])
assert helpers.approx_equal(som_.activate([1, 3]), [2, 2])
assert helpers.approx_equal(som_.activate([0.2, 1.6]), [1, 1])
assert helpers.approx_equal(som_.activate([1.8, 1.6]), [1, 1])
def test_SOM_activate_matrix():
som_ = som.SOM(2, 2)
som_._weights = numpy.ones(som_._weights.shape)
assert helpers.approx_equal(som_.activate([[1, 1], [0, 1]]),
[[0, 0], [1, 1]])
assert helpers.approx_equal(som_.activate([[1, 0], [1, 2]]),
[[1, 1], [1, 1]])
assert helpers.approx_equal(som_.activate([[1, 3], [0.2, 1.6]]),
[[2, 2], [1, 1]])
assert helpers.approx_equal(som_.activate(
[[1.8, 1.6], [0,
0]]), [[1, 1], [1.4142135623730951, 1.4142135623730951]])
def test_som_reduces_distances_matrix():
# SOM functions correctly if is moves neurons towards inputs
input_matrix, target_matrix = datasets.get_xor()
# Small initial weight range chosen so network isn't "accidentally"
# very close to inputs initially (which could cause test to fail)
num_neurons = random.randint(2, 10)
som_ = som.SOM(2, num_neurons, initial_weights_range=0.25)
# Convenience function
def min_distances():
distance_matrix = som_.activate(input_matrix)
assert distance_matrix.shape == (len(input_matrix), num_neurons)
return numpy.min(distance_matrix, axis=-1)
# Train SOM
# Assert that distances have decreased
all_closest = min_distances()
som_.train(input_matrix, target_matrix, iterations=20)
new_closest = min_distances()
print all_closest
print new_closest
for old_c, new_c in zip(all_closest, new_closest):
assert new_c < old_c